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Data Science Library

Choose from a library of reusable, best in class statistical and machine learning models which have been optimised for commercial use cases and data formats.

NStack's platform automatically validates model output and performance and scales out in the cloud.

Subscription Churn Prediction

Predict which subscribers have a high propensity to churn, by inputting usage data and subscription information. Output includes customer risk score, and weighting of input variables (i.e. which inputs contributed to health) so a custom action can be taken.

Inputs

Usage Data, Subscription Data

Outputs

Churn Hazard Score, Churn Indicators

Data-Driven Attribution Model

Understanding the series of touch points which lead to conversion can help marketing teams assign spend more effectively across channels. The NStack attribution model takes in historical data to score traffic sources based on their performance.

Inputs

Usage Data, Subscription Data

Outputs

DDA value, Last/First touch value

E-Commerce Churn Prediction

Use transactional history to predict the probability that a given customer is alive, based on their historical purchasing patterns and the patterns of other customers.

Inputs

Transactional Data

Outputs

Churn Hazard Score

Product Ranking Model

Machine-learning based model which trains on past customer interactions and CRM data to decide which products to show to a given customer.

Inputs

Usage Data, Purchase Data

Outputs

Ranking of products

Recommendation System

Use historical information on browsing or purchasing patterns to suggest relevant products to users, or find which products or brands are similar to one and other.

Inputs

Usage Data, Purchase Data

Outputs

Similar Products, Relevant Products

Lifetime Value Prediction

Predict the Lifetime Value of a customer based on their past purchase history and the trends in purchase history across the entire customer base.

Inputs

Transactional Data

Outputs

Customer Lifetime Value

Drag-and-Drop Workflows

Use NStack's web-based builder to create drag-and-drop workflows that connect together data sources, models, and processing steps.

Integrations

Connectors into data warehouses, analytics platforms, marketing tools, and CRM allow you to integrate data science directly into the business.

Processing Steps

Built in pre- and post-processing steps such as bucketing, filtering, or aggregations to build powerful automated workflows.

Scheduler

Use NStack's scheduler to run workflows automatically at regular cadences.

SQL and Python Support

Quickly write custom processing steps in Python or SQL for more powerful and flexible workflows.

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